class_docstr: AdaGrad optimizer.
  
  This class implements the AdaGrad optimizer described in *Adaptive Subgradient
  Methods for Online Learning and Stochastic Optimization*, and available at
  http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf.
  
  This optimizer updates each weight by::
  
      grad = clip(grad * rescale_grad, clip_gradient) + wd * weight
      history += square(grad)
      weight -= learning_rate * grad / (sqrt(history) + epsilon)
  
  This optimizer accepts the following parameters in addition to those accepted
  by :class:`.Optimizer`.
  
  See Also
  ----------
  :meth:`mxnet.ndarray.sparse.adagrad_update`.
  
  Parameters
  ----------
  learning_rate : float, default 0.01
      The initial learning rate. If None, the optimization will use the
      learning rate from ``lr_scheduler``. If not None, it will overwrite
      the learning rate in ``lr_scheduler``. If None and ``lr_scheduler``
      is also None, then it will be set to 0.01 by default.
  epsilon : float, default 1e-6
      Small value to avoid division by 0.
  use_fused_step : bool, default True
      Whether or not to use fused kernels for optimizer.
      When use_fused_step=False or grad is not sparse, step is called,
      otherwise, fused_step is called.
docstr: Perform a fused optimization step using gradients and states.
  Fused kernel is used for update.
  
  Parameters
  ----------
  indices : list of int
      List of unique indices of the parameters into the individual learning rates
      and weight decays. Learning rates and weight decay may be set via `set_lr_mult()`
      and `set_wd_mult()`, respectively.
  weights : list of NDArray
      List of parameters to be updated.
  grads : list of NDArray
      List of gradients of the objective with respect to this parameter.
  states : List of any obj
      List of state returned by `create_state()`.
docstr_tok: ["Perform","a","fused","optimization","step","using","gradients","and","states",".","Fused","kernel","is","used","for","update",".","Parameters","--","--","--","--","--","indices",":","list","of","int","List","of","unique","indices","of","the","parameters","into","the","individual","learning","rates","and","weight","decays",".","Learning","rates","and","weight","decay","may","be","set","via","`","set_lr_mult","(",")","`","and","`","set_wd_mult","(",")","`",",","respectively",".","weights",":","list","of","NDArray","List","of","parameters","to","be","updated",".","grads",":","list","of","NDArray","List","of","gradients","of","the","objective","with","respect","to","this","parameter",".","states",":","List","of","any","obj","List","of","state","returned","by","`","create_state","(",")","`","."]
code: def fused_step(self, indices, weights, grads, states):
      """Perform a fused optimization step using gradients and states.
      Fused kernel is used for update.
  
      Parameters
      ----------
      indices : list of int
          List of unique indices of the parameters into the individual learning rates
          and weight decays. Learning rates and weight decay may be set via `set_lr_mult()`
          and `set_wd_mult()`, respectively.
      weights : list of NDArray
          List of parameters to be updated.
      grads : list of NDArray
          List of gradients of the objective with respect to this parameter.
      states : List of any obj
          List of state returned by `create_state()`.
      """
      for index, weight, grad, state in zip(
          indices, weights, grads, states
      ):
          is_sparse = grad.stype == "row_sparse"
  
          if is_sparse:
              self._update_count(index)
              lr = self._get_lr(index)
              wd = self._get_wd(index)
              kwargs = {
                  "epsilon": self.epsilon,
                  "rescale_grad": self.rescale_grad,
              }
              if self.clip_gradient:
                  kwargs["clip_gradient"] = self.clip_gradient
  
              history = state
  
              # When grad is sparse, update weight with fused kernel
              sparse.adagrad_update(
                  weight,
                  grad,
                  history,
                  out=weight,
                  lr=lr,
                  wd=wd,
                  **kwargs,
              )
          else:
              # When the grad is not sparse, the func step is called to update weight and state
              self.step([index], [weight], [grad], [state])
  
file_path: mxnet/python/mxnet/optimizer/adagrad.py
imports: from __future__ import absolute_import
  from ..ndarray import zeros, clip, sqrt, square
  from ..ndarray import sparse
  from .optimizer import Optimizer, register
label_desc: Use image node_id 4 for calling the AdaGrad obj's underlying member method code with example usage: obj.fused_step(indices, weights, grads, states) without return types